Impacts of Sampling Design on Estimates of Microbial Community Diversity and Composition in Agricultural Soils

Abstract

Soil microbiota play important and diverse roles in agricultural crop nutrition and productivity. Yet, despite increasing efforts to characterize soil bacterial and fungal assemblages, it is challenging to disentangle the influences of sampling design on assessments of communities. Here, we sought to determine whether composite samples—often analyzed as a low cost and effort alternative to replicated individual samples—provide representative summary estimates of microbial communities. At three Minnesota agricultural research sites planted with an oat cover crop, we conducted amplicon sequencing for soil bacterial and fungal communities (16SV4 and ITS2) of replicated individual or homogenized composite soil samples. We compared soil microbiota from within and among plots and then among agricultural sites using both sampling strategies. Results indicated that single or multiple replicated individual samples, or a composite sample from each plot, were sufficient for distinguishing broad site-level macroecological differences among bacterial and fungal communities. Analysis of a single sample per plot captured only a small fraction of the distinct OTUs, diversity, and compositional variability detected in the analysis of multiple individual samples or a single composite sample. Likewise, composite samples captured only a fraction of the diversity represented by the six individual samples from which they were formed, and, on average, analysis of two or three individual samples offered greater compositional coverage (i.e., greater number of OTUs) than a single composite sample. We conclude that sampling design significantly impacts estimates of bacterial and fungal communities even in homogeneously managed agricultural soils, and our findings indicate that while either strategy may be sufficient for broad macroecological investigations, composites may be a poor substitute for replicated samples at finer spatial scales.